In the history of software development, a deep, problematic divide existed between the teams that built software and the teams that managed it. This divide was often called the “wall of confusion.” On one side, the Development (Dev) team was incentivized to create and release new features as quickly as possible. Their success was measured by change and innovation. On the other side, the Operations (Ops) team was incentivized to maintain stability, reliability, and security. Their success was measured by uptime and the absence of failure. This fundamental conflict of interest meant that new releases were a source of friction, blame, and painful, late-night troubleshooting. DevOps emerged as a cultural and professional movement to break down this wall. It is not a single tool or a specific job title but a software engineering methodology and philosophy. Its primary goal is to improve collaboration between development and operations, fostering a culture of shared responsibility. By integrating these two traditionally separate functions, organizations can accelerate their software development and deployment processes, enabling faster, more reliable, and more frequent delivery of high-quality software. This shift moves the entire organization from a state of conflict to one of aligned goals, where everyone is responsible for both innovation and stability.
The Core Philosophy: The Three Ways
The DevOps philosophy is often articulated through “The Three Ways,” a set of guiding principles. The First Way is about Systems Thinking. It emphasizes the performance of the entire system, from development to production, rather than the performance of a single department. The goal is to maximize the flow of work from left to right, from idea to reality. This involves limiting “work in progress,” reducing batch sizes, and automating relentlessly to remove constraints and bottlenecks. The aim is to make the delivery process fast, smooth, and predictable, ensuring that a developer’s code can move quickly and safely into the hands of a user. The Second Way is about Amplifying Feedback Loops. This principle stresses the importance of rapid and constant feedback from the right side (Operations) back to the left side (Development). By creating faster and more informative feedback, the team can identify and fix problems closer to when they were created. This prevents issues from cascading downstream where they are much more difficult and expensive to resolve. This principle is embodied in practices like automated testing, continuous integration, and comprehensive monitoring. The Third Way is about a Culture of Continuous Experimentation and Learning. This principle encourages taking risks and learning from failure. It fosters a high-trust environment where ideas are encouraged, and failures are treated as opportunities to learn, not to blame. This allows the organization to innovate, adapt, and improve relentlessly, leading to a state of mastery and organizational resilience.
Pillar 1: Continuous Integration
Continuous Integration (CI) is a foundational technical practice of DevOps. At its core, CI is an automated process where developers frequently merge their code changes into a central repository. After each merge, an automated build and test sequence is triggered. This practice is a direct response to the problems of “merge hell,” where multiple developers working in isolation on separate branches for weeks or months would find it nearly impossible to integrate their changes later. The conflicts would be numerous, and the resulting bugs would be difficult to trace. By integrating small changes multiple times per day, the team can detect and resolve integration issues almost immediately. The automated build compiles the code, and the automated test suite—comprising unit tests, integration tests, and other checks—validates that the new code has not broken any existing functionality. If the build or tests fail, the CI system provides immediate feedback to the entire team. This practice ensures that the main codebase is always in a healthy, buildable, and testable state. It enforces code quality, reduces risk, and builds a foundation of confidence for the next step: continuous delivery.
Pillar 2: Continuous Delivery and Deployment
Continuous Delivery (CD) is the logical extension of Continuous Integration. It is a practice where code changes that have passed all automated tests in the CI stage are automatically prepared and packaged for a release to a production environment. The output of the CI/CD pipeline is a built artifact (like a binary or a container image) that has been validated and is ready to be deployed at any time. The primary goal of Continuous Delivery is to ensure that the software is always in a releasable state. The decision to “go live” with a new release might still be a manual one, often a simple push of a button, allowing the business to decide the timing based on market needs or other factors. Continuous Deployment is a more advanced step beyond Continuous Delivery. In a Continuous Deployment model, every change that passes the full automated test suite is automatically deployed all the way to production, with no human intervention. This is the ultimate expression of confidence in the automated pipeline. Large technology companies often use this practice to deploy new software features to their users multiple times per day. This approach maximizes the speed of delivery, shortens the feedback loop from users to developers, and allows for rapid iteration and experimentation. Both practices rely on a robust and trustworthy automated pipeline to maintain stability.
Pillar 3: Infrastructure as Code
Infrastructure as Code (IaC) is a core component of DevOps that addresses the problem of managing and provisioning the underlying IT infrastructure. In the past, servers, networks, and databases were provisioned manually. This process was slow, expensive, error-prone, and nearly impossible to replicate accurately. One server might be configured slightly differently from another, leading to a “works on my machine” problem, or worse, a “works in staging but not in production” crisis. IaC solves this by managing infrastructure through code and configuration files. Instead of manually clicking through a web console, an operations engineer writes a declarative file that specifies what the infrastructure should look like. This code is then stored in a source control repository, just like application code. It can be versioned, reviewed, and tested. When the code is executed, an IaC tool automatically provisions and configures the infrastructure to match the specification. If a change is needed, the code file is edited, not the live server. This makes the infrastructure consistent, repeatable, and stable. A new environment, whether for testing, staging, or disaster recovery, can be set up within minutes and without manual effort, perfectly matching the production environment. This practice is a critical enabler for the automation, speed, and scalability that DevOps promises.
Pillar 4: Monitoring and Observability
In the DevOps world, deployment is not the end of the journey; it is the beginning of the feedback loop. Monitoring and Observability are the practices that provide insight into the system’s performance and health once it is running in production. Traditional monitoring focused on “known unknowns”—things you knew you needed to watch, like CPU usage, memory, and disk space. If the CPU spiked, an alert would fire. This is still necessary, but it is no longer sufficient for complex, distributed systems. Observability, by contrast, is about “unknown unknowns”—the ability to ask new questions about your system’s behavior without having to pre-define a new metric or dashboard. Observability is often described as having three pillars: metrics, logs, and traces. Metrics are the time-series numerical data, like “requests per second.” Logs are the detailed, timestamped event records, like “user X failed to log in.” Traces are a detailed view of a single request as it travels through all the different microservices in your application, showing you exactly where time was spent and where an error occurred. A strong culture of observability, supported by real-time monitoring tools and dashboards, allows teams to monitor system performance, identify problems as early as possible, and resolve them quickly, thus fulfilling the shared responsibility for production stability.
The DevOps Culture: Shared Responsibility
More than any tool or technical practice, DevOps is a cultural shift. It is about creating a culture of collaboration and shared responsibility. It tears down the “wall of confusion” and forces development and operations teams to work together with a single, aligned goal: the fast and reliable delivery of value to the customer. In a healthy DevOps culture, developers are not just responsible for writing code; they are also responsible for how that code behaves in production. Operations teams are not just “gatekeepers” of production; they are involved early in the development cycle, providing expertise on scalability, reliability, and infrastructure. This culture is most visibly embodied in the practice of a “blameless postmortem.” When something inevitably goes wrong in production, the goal is not to find the person who caused the problem, but to find the systemic reason the problem was able to occur. The focus is on improving the process, the automation, and the system’s resilience to prevent that entire class of failure from happening again. This high-trust environment encourages people to report problems, experiment, and learn, which is the engine of continuous improvement. This cultural foundation is what makes the technical practices of CI/CD, IaC, and monitoring truly effective.
Why Machine Learning Breaks Traditional DevOps
For decades, the DevOps methodology has been refined to manage the software development lifecycle. This lifecycle is fundamentally code-centric. Its behavior is deterministic; given the same input, a well-written piece of software will produce the same output every time. Its artifacts are source code, compiled binaries, and configuration files. But when companies started to integrate machine learning (ML) models into their products, they quickly discovered that this established paradigm breaks down. Machine learning systems are not just code; they are a complex combination of code, data, and a trained model. The behavior of an ML system is not deterministic; it is probabilistic. It learns from data, and its performance is entirely dependent on the quality, volume, and relevance of that data. The model itself, the primary artifact, is not “written” but “trained.” This introduces a new set of challenges that traditional DevOps was not designed to handle. How do you version a 50-gigabyte dataset? How do you test a model’s “accuracy” in an automated pipeline? How do you monitor a model that is silently becoming less accurate in production? It became clear that a new, specialized extension of DevOps was required.
Introducing MLOps: DevOps for Machine Learning
MLOps, or Machine Learning Operations, is the answer to these challenges. It is a set of practices, building on the principles of DevOps, designed to continuously, reliably, and efficiently deploy and maintain machine learning models in production. MLOps extends the DevOps culture and technical practices to cover the entire machine learning lifecycle. The core goal of MLOps is to automate and streamline all phases of an ML project, from data acquisition and model training to final deployment and long-term monitoring. It aims to bridge the gap between the experimental, research-oriented world of data science and the stable, reliable world of IT operations. Why is this necessary? Developers and data scientists often face temptation to ignore clean processes to get a model into production quickly. This initial “shortcut” carries immense risk. As with traditional software, ignoring best practices early on leads to increasing complexity and reduced maintainability over time. This problem is magnified in ML systems, where managing data dependencies, evolving models, and feedback loops presents a unique and difficult challenge. Without MLOps, this initial shortcut almost always leads to a brittle, unmanageable system that cannot be iterated upon, reproduced, or trusted.
The Hidden Technical Debt in Machine Learning
A famous and influential research paper highlighted the concept of “Hidden Technical Debt in Machine Learning Systems.” It argued that ML systems accumulate technical debt in ways that traditional software does not. This “debt” is the long-term cost of choosing an easy, fast solution now over a better, more sustainable approach. In ML, this debt accumulates in unique areas. One of the biggest is the data dependency problem. A model’s performance is inextricably linked to the data it was trained on. If the input data in production starts to look different from the training data, the model’s performance will degrade silently. Other sources of debt include complex data pipelines that are difficult to update, feedback loops where a model’s own predictions influence the new data it gets trained on, and a lack of process for reproducing a model. Without MLOps, a company might find itself in a situation where a data scientist who built a critical model leaves the company, and no one else can figure out what data, what code, and what “hyperparameters” were used to create it. The model is a “black box” that works, but can never be updated or fixed. MLOps principles are designed specifically to find and pay down this hidden debt.
The Full Machine Learning Lifecycle
MLOps must manage a lifecycle that is far more complex and cyclical than a traditional software lifecycle. It begins with data. This phase involves data acquisition, ingestion, and validation to ensure the data is clean and correct. This is often managed by data engineers. Next is the model training phase, which is the domain of the data scientist. This is not a linear process but an iterative cycle of experimentation. A data scientist will try different data preprocessing steps, feature engineering techniques, model architectures, and “hyperparameters” (the settings for the training process) to find the best-performing model. Once a “candidate” model is produced, it must be validated—not just for its predictive accuracy, but also for its fairness, lack of bias, and performance against specific business goals. If the model is approved, it is packaged and deployed into production by an ML engineer, often as an API that can be called by other applications. But the journey is not over. The final, critical phase is monitoring. The model’s performance in production must be continuously monitored for both operational health (like latency) and, more importantly, statistical health (like accuracy and drift). When performance degrades, a feedback loop is triggered, starting the entire cycle over again, often by automatically retraining the model on new, fresh data.
Core Component 1: The Experimental Nature of ML
A key difference from traditional software is that machine learning is inherently experimental. A developer knows what their code is supposed to do. A data scientist does not know which model or which data will produce the best result; they must discover it. A data scientist might run hundreds of “experiments” to find a single, production-worthy model. In this process, they are tracking many variables: the version of the code, the version of the dataset, the preprocessing steps, the model’s hyperparameters, and the resulting performance metrics like accuracy or precision. A core component of MLOps is the experiment tracker. This is a specialized tool, like a lab notebook for data scientists. It automatically logs all of these variables for every single training run. This is essential for reproducibility. If an experiment from three weeks ago suddenly looks promising, the data scientist can look it up in the tracker and know exactly what combination of factors produced that result. This prevents wasted time, ensures that good results can be reproduced, and allows for a systematic, scientific approach to model development rather than a chaotic, disorganized one.
Core Component 2: Data and Model Versioning
In DevOps, version control systems for code are the “single source of truth.” If a bug is introduced, you can look at the code history and roll back to a previous version. This concept is essential for MLOps, but it is insufficient. MLOps must manage the lifecycle of both software code and data-driven artifacts. The problem is that traditional code versioning systems were not designed to handle the artifacts of machine learning. They work well for small text files, but they fail completely when faced with a 100-gigabyte dataset or a 2-gigabyte model file. MLOps introduces specialized data version control tools. These tools work alongside your code repository, allowing you to create a “snapshot” of your data at a specific point in time and “version” it, much like you version your code. This is critically important. To truly reproduce a model, you need three things: the code that trained it, the data it was trained on, and the hyperparameters used. Data versioning solves the second piece of that puzzle. Similarly, MLOps includes model registries, which are systems for versioning the output of the training process—the model itself. This allows a team to track different model versions, manage their lifecycle (e.g., “staging,” “production,” “archived”), and quickly roll back to a previous, better-performing model if a new one fails in production.
Core Component 3: The ML Pipeline and Orchestration
Because the ML lifecycle is so complex and cyclical, MLOps relies heavily on automation through pipelines. A DevOps CI/CD pipeline is often a single, linear process: Build -> Test -> Deploy. An MLOps system involves multiple, interconnected pipelines. There is often a data pipeline (or CI pipeline for data) that automatically ingests, validates, and preprocesses new data. Then there is a model training pipeline (or Continuous Training, CT) that can be triggered to automatically train, validate, and test a new model on this new data. Finally, there is a deployment pipeline (CD) that, if the new model passes all tests, can automatically deploy it to production. Managing this complex web of pipelines requires a pipeline orchestration tool. These platforms are designed to define, schedule, and monitor these complex, multi-step workflows. They can handle dependencies (e.g., “only run the training pipeline after the data validation pipeline succeeds”), manage the specialized infrastructure required (like a cluster of machines with powerful graphics processors, or GPUs), and provide a central place to monitor the health of the entire automated system. This automation of the entire lifecycle, not just the code deployment, is the central technical goal of MLOps.
Core Component 4: Monitoring for Model and Data Drift
In DevOps, monitoring is focused on operational health: “Is the application running?” and “Is it fast?” MLOps must do this too, but it has a second, much more difficult monitoring challenge: “Is the model still correct?” A software application, once deployed, has predictable behavior. An ML model, once deployed, has a performance that will almost certainly degrade over time. The world changes, and the new, live data coming into the model will slowly but surely begin to look different from the static data it was trained on. This is called drift. MLOps monitoring systems are designed to detect two types of drift. The first is data drift. This is a statistical change in the input data. For example, a loan approval model trained on pre-pandemic data might start seeing very different income and employment patterns during an economic downturn. The model’s assumptions are no longer valid. The second is concept drift. This is when the meaning of the data itself changes. For example, in a fraud detection system, “fraud” is a “concept” defined by the actions of malicious actors. As fraudsters change their techniques to avoid detection, the very concept of fraud shifts, and the model must be retrained to learn these new patterns. A robust MLOps monitoring system will automatically detect this drift and trigger an alert or even an automated retraining pipeline.
The Core Focus: Code-Centric vs. Model-Centric
The most fundamental difference between DevOps and MLOps lies in their primary focus. DevOps is fundamentally code-centric. The entire methodology is designed to optimize the development, testing, and deployment of software applications. The “artifact” at the center of the universe is the application’s source code, which is compiled into a predictable, executable binary. The behavior of the application is deterministic and defined by its code. If the code is correct, the application will perform its function reliably and consistently. The main challenge is managing the complexity of this code and its deployment. MLOps, in contrast, is data- and model-centric. While code is still a critical component, it is no longer the only component. In many ML systems, the code for the model architecture might be relatively stable, while the data is changing constantly. The central artifact is the trained model, which is not written by a human but is the output of a complex training process. The behavior of this model is not deterministic but probabilistic. The main challenge for MLOps is not just managing the code, but managing the entire workflow that creates and maintains this data-driven model. The focus shifts from “is the application running?” to “is the model’s behavior still correct and valuable?”
Lifecycle Disparities: Predictable vs. Probabilistic
The lifecycle of a traditional software application, as managed by DevOps, is relatively linear and predictable. A developer writes code for a new feature, a CI/CD pipeline builds and tests it, and it gets deployed. Once in production, the behavior of this new feature is static and predictable. It will not “decay” or become less correct over time on its own. A bug might be discovered, but the feature itself does not degrade simply because time has passed. The lifecycle is code-oriented, and updates are driven by new feature requests or bug fixes. The MLOps lifecycle is highly iterative, cyclical, and probabilistic. It is an experimental process of data preprocessing, feature engineering, model training, and retraining. A model’s performance is not guaranteed. It is a statistical probability, and this probability changes as the real-world data it ingests changes. A model deployed in production has a limited shelf life. Its behavior is not static; it is dynamic and will almost certainly decay as data drift or concept drift occurs. The lifecycle is therefore data-driven. Updates are not just triggered by new feature requests, but by a continuous monitoring system that detects performance degradation, automatically triggering a retraining cycle.
Managing Artifacts: Binaries vs. Intelligent Artifacts
In a DevOps workflow, the artifacts being managed are primarily software artifacts. These include the source code itself, which is stored in a code repository, as well as the compiled binaries, container images, and configuration files that are the output of the build process. These artifacts are generally static and self-contained. A container image, for example, packages the application and all its dependencies, and this package is what gets promoted from development to testing to production. MLOps must manage all of these plus a new class of ML-specific artifacts. These artifacts are dynamic and often enormous. The first new artifact is the dataset itself. MLOps must have a strategy for versioning datasets, which can be terabytes in size. The second new artifact is the trained model. A modern deep learning model can be many gigabytes, and an MLOps system needs a model registry to version these models and track their lineage. The third artifact is the metadata from experiments—the log of all hyperparameters, metrics, and parameters from every training run. This is why MLOps extends DevOps: it must manage the lifecycle of both the software code and these complex, data-driven artifacts.
The Challenge of Versioning: Code vs. Data
Versioning is a solved problem in DevOps. Source control systems are the indisputable single source of truth. They are masters at handling small text files, tracking line-by-line changes, and merging the work of multiple developers. For a DevOps practitioner, “version control” is synonymous with “code version control.” If you want to reproduce a previous version of the application, you simply check out the corresponding version of the code from the repository and re-run the build pipeline. This process is reliable and well-understood. In MLOps, this is not enough. To truly reproduce a model, you need to version three things: the code (for training and feature engineering), the data used for training, and the configuration (like hyperparameters). Traditional code versioning systems are not designed to store or version 50-gigabyte datasets. Therefore, MLOps requires a new set of tools, often called data version control systems. These tools work in conjunction with code repositories to “snapshot” large datasets without having to store them directly in the repository. This allows a data scientist to “check out” a specific version of the code and the specific version of the data that was used to train a model, making the experiment fully reproducible.
The Testing Paradigm Shift
Testing in a DevOps world is a mature discipline. It focuses on validating the correctness of the code. This includes unit tests (testing a single function), integration tests (testing how different modules interact), end-to-end tests (testing a full user workflow), and performance tests (testing load and latency). These tests are typically binary: they either pass or they fail. This makes them perfect for a fully automated CI pipeline. If all tests pass, the code is considered “good.” MLOps must incorporate all of these traditional software tests—the model’s API still needs unit tests and integration tests. However, it must also add a new, complex layer of ML-specific testing. This includes data validation, which tests the incoming data for correctness, schema, and statistical properties. It includes model validation, which tests the trained model’s predictive accuracy against a held-out test dataset. And increasingly, it includes fairness and bias testing, which evaluates whether the model’s predictions are equitable across different subgroups (e.g., by race, gender, or age). These tests are not always binary; a model’s accuracy might be “acceptable” but not perfect, requiring a human to make a judgment call.
Pipelines: CI/CD vs. CT/CM/CD
The DevOps workflow is defined by the CI/CD pipeline, which stands for Continuous Integration and Continuous Delivery/Deployment. This is a largely linear flow that is triggered by a code change. A developer commits new code, which triggers the pipeline to build, test, and deploy the application. The primary goal is to get new code into production quickly and safely. MLOps adopts this pipeline but finds it insufficient. The MLOps world introduces new types of pipelines, often creating a directed acyclic graph (DAG) of workflows. First, MLOps often includes a data pipeline for data ingestion and validation. More importantly, it adds the concept of Continuous Training (CT). A training pipeline is not just triggered by a code change; it can be triggered by new data becoming available or by a schedule (e.g., “retrain the model every night”). It also adds Continuous Monitoring (CM) as an active component. This monitoring system can, in turn, automatically trigger the Continuous Training pipeline when it detects that the production model’s performance has degraded, creating a closed-loop system.
Infrastructure Requirements: CPUs vs. Specialized Hardware
The infrastructure requirements for most traditional software applications, managed by DevOps, are relatively standard. They consist of web servers, application servers, and databases. These workloads run on general-purpose Central Processing Units (CPUs). The primary scaling challenge is often “horizontal”—that is, handling more and more concurrent users by simply adding more web servers, which are often stateless and interchangeable. This is a well-understood problem solved by load balancers and auto-scaling groups. MLOps introduces a new and significant infrastructure challenge: the need for specialized hardware. The training process for modern machine learning, especially deep learning, is computationally massive and cannot be done efficiently on CPUs. It requires Graphics Processing Units (GPUs) or even more specialized hardware like Tensor Processing Units (TPUs). This hardware is expensive and must be managed carefully. An MLOps pipeline, therefore, must be able to provision and manage these specialized resources on demand, running a training job on a GPU cluster and then spinning it down to save costs. The model serving (inference) phase may also require GPUs for low-latency predictions, adding further complexity to the production infrastructure.
Team Structure and Skillsets
The DevOps culture was created to foster collaboration between two primary groups: software engineers (Dev) and operations engineers (Ops). Software engineers focus on programming and feature development, while operations teams focus on infrastructure, reliability, and scale. The separation of roles, while bridged by collaboration, is still quite clear. MLOps projects require a much larger and more diverse team, bringing together multiple disciplines that must collaborate. This team includes data scientists, who are the subject matter experts and are focused on research, experimentation, and model performance. It includes data engineers, who are responsible for building the data pipelines that feed the system. It includes machine learning engineers, a specialized role that bridges data science and software engineering, focusing on production-izing and scaling the model. And finally, it includes the traditional operations teams who manage the underlying infrastructure. MLOps is the cultural and technical framework that attempts to get all these different groups, each with their own priorities and skills, to speak the same language and work toward a single goal.
The Shared Foundation: The DevOps Philosophy
Before MLOps was a distinct term, it was simply the set of practices that teams discovered they needed when they tried to apply a DevOps philosophy to their machine learning projects. MLOps is not a replacement for DevOps; it is a superset or an extension of it. The core philosophy that drives DevOps is the exact same philosophy that drives MLOps. This foundation is built on the “Three Ways”: systems thinking, amplifying feedback loops, and a culture of continuous learning. Both disciplines focus on breaking down silos between teams to improve collaboration. Both aim to automate workflows to increase speed and efficiency. Both are obsessed with quality, reliability, and scalability. MLOps takes these shared principles and extends them to address the unique challenges of machine learning. Where DevOps focuses on the “flow” of code, MLOps expands the definition of “flow” to include data and models. Where DevOps creates feedback loops from production monitoring, MLOps adds new feedback loops for model drift. This shared philosophical foundation is the most important overlap between the two.
Automation Everywhere: The CI/CD Pipeline as a Core
Both disciplines rely heavily on automated CI/CD pipelines to manage their workflows. At the end of the day, an ML application is still a software application. There is a significant amount of code that needs to be built, tested, and deployed, all of which is a pure DevOps task. This includes the code for the data processing, the feature engineering, the model training, and the API that will ultimately serve the model’s predictions. All of this code must be stored in a source control repository. MLOps workflows use a traditional CI/CD pipeline to manage this code. When a data scientist changes a feature engineering script, it should trigger a CI pipeline that runs unit tests on that script. When an ML engineer updates the model’s API code, it should trigger a CI/CD pipeline that builds a new container, runs integration tests, and deploys the new API. MLOps simply extends this concept. It adds new types of pipelines (like data validation and model training) and new triggers for these pipelines (like new data), but the fundamental practice of automated, pipeline-driven workflows is taken directly from the DevOps playbook.
Infrastructure as Code (IaC) in Both Worlds
A critical enabler for automation in both DevOps and MLOps is Infrastructure as Code (IaC). Both disciplines recognize that managing infrastructure manually is slow, unreliable, and unscalable. They both use IaC principles to provision, manage, and scale all the components of their infrastructure, from servers and databases to networks and load balancers. The configuration files for this infrastructure are stored in source control, just like application code, so they can be versioned, reviewed, and deployed automatically. The tools and practices are identical. An operations engineer on a DevOps team might use an IaC tool to define a cluster of web servers. An ML engineer on an MLOps team will use the exact same tool to define the infrastructure for their ML workloads. This might include provisioning the storage for datasets, defining the data pipeline orchestration server, or creating a scalable cluster for model serving. MLOps simply has additional infrastructure needs, such as provisioning and managing expensive GPU clusters for training, but the way it manages this infrastructure—as code—is a practice adopted directly from DevOps.
Containerization and Orchestration: A Universal Need
One of the biggest challenges in all of software is the “works on my machine” problem. A developer’s laptop has a different operating system, different libraries, and different dependencies than the production server, leading to bugs that are impossible to reproduce. DevOps largely solved this problem through the widespread adoption of containerization. Containers bundle an application’s code with all its dependencies—libraries, configuration files, and runtimes—into a single, lightweight, and portable image. This container will run exactly the same way on a laptop, a test server, or in production. This technology is even more critical for MLOps. The environment for machine learning is notoriously complex, with specific versions of data science libraries, drivers for specialized hardware like GPUs, and complex dependencies. A data scientist might build a model using one version of a library, and the production server might have another, leading to a model that fails or, worse, gives different predictions. MLOps uses containers to solve this. The training process itself is often run inside a container to ensure a reproducible environment. The final trained model is then packaged inside a container, along with its API, to be deployed. Both DevOps and MLOps then use container orchestration platforms to manage, scale, and schedule these containers in production.
Source Control as the Single Source of Truth
A fundamental tenet of DevOps is that the source control repository is the “single source of truth” for the system. All application code, all build scripts, all CI/CD pipeline definitions, and all Infrastructure as Code configurations are stored and versioned in this repository. Nothing is done manually. To understand the state of the system or to make a change, you go to the repository. This principle is what enables automation, collaboration, and auditing. MLOps adopts this principle wholeheartedly. The code repository is still the single source of truth for everything that is code. This includes the ML model’s training code, the data processing scripts, the API code, the pipeline definitions, and the IaC files. MLOps simply acknowledges that this is not the complete picture. It must be supplemented with other systems that act as the source of truth for the artifacts that are not code. This includes data version control systems as the source of truth for data, and model registries as the source of truth for trained models. The MLOps “source of truth” is a federated one, but the core idea of a centralized, versioned, and auditable system is identical to the DevOps principle.
Monitoring and Observability: The Shared Goal
Both DevOps and MLOps rely on robust monitoring systems to ensure reliability and catch problems as early as possible. The goal is the same: to gain real-time insight into the health and performance of the production system. Many of the tools used are even the same. Both disciplines use monitoring systems and visualization dashboards to track application performance, system-level metrics (like CPU and memory), application uptime, and to aggregate logs for troubleshooting. This is another area where MLOps simply adds to the DevOps requirements. A DevOps team monitors for application errors and latency. The MLOps team must monitor for those plus a new class of ML-specific metrics. They must monitor the model’s predictive accuracy, its prediction latency, and, most importantly, the statistical properties of the data it is receiving to detect drift. The MLOps monitoring dashboard will have all the standard operational metrics from DevOps, right alongside new, specialized dashboards for tracking model quality and data health. This shared need for observability makes the collaboration between the teams much more effective.
Building on DevOps: MLOps as an Extension, Not a Replacement
It should be clear that MLOps is not a competitor to DevOps. You do not choose one or the other. An organization that wants to successfully deploy machine learning models must, by necessity, be good at DevOps first. MLOps builds upon and extends DevOps. A machine learning model is useless in isolation; it is delivered to users as part of a software application. That application—its API, its web front-end, its database—must be built, tested, and managed using standard DevOps practices. The MLOps extensions only apply to the data and model components of that system. You can think of it as a specialized set of practices within a larger DevOps culture. The team responsible for the web application’s front-end will follow a pure DevOps workflow. The team responsible for the recommendation engine’s model will follow an MLOps workflow, but they will still rely on the company’s shared DevOps platform for CI/CD, IaC, and container orchestration. The two disciplines are deeply intertwined and share the same ultimate goal: delivering high-quality, reliable, and valuable software to users.
DevOps in Action: The E-Commerce Platform
Let’s consider a practical use case for a pure DevOps workflow: an e-commerce platform. The platform is a traditional three-tier web application with a web front-end, a back-end API, and a database. The development team is tasked with adding a new feature: a “Flash Sale” component for the homepage. The developer writes the code for the new API endpoint and the front-end user interface. When they are ready, they commit their code to the central source control repository. This commit automatically triggers the CI/CD pipeline, which is the heart of the DevOps process. The pipeline first runs a series of automated unit tests and integration tests. If they pass, it packages the front-end and back-end code into separate container images. These images are then automatically deployed to a “staging” environment, which is an exact replica of production, created using Infrastructure as Code (IaC). An automated end-to-end test then runs, simulating a user visiting the homepage and interacting with the flash sale. If all these checks pass, the pipeline signals that the feature is ready. A product manager can then push a single button to “promote” this release, and the pipeline’s continuous delivery mechanism deploys the new containers to production with zero downtime, perhaps using a blue-green deployment strategy. The entire process is fast, safe, and automated.
DevOps in Action: The B2B SaaS Application
Another classic use case for DevOps is a multi-tenant Software-as-a-Service (SaaS) tool, such as a project management or accounting platform. In this business model, a single application serves hundreds or thousands of different “tenant” companies, each with their own isolated data. The demands for reliability, security, and scalability are extremely high. DevOps is not just an advantage here; it is a requirement for survival. When a new customer signs up for the service, the DevOps automation, driven by IaC, must provision an entire new, isolated environment for them. This includes creating a new database schema, setting up their unique subdomain, and configuring their access controls, all without any human intervention. Furthermore, when the development team wants to release a bug fix, they cannot afford downtime that would affect all their customers. They use a robust CI/CD pipeline to test the fix extensively. The deployment might be “staged,” rolling out the new version to a small percentage of internal users first, then to 1% of customers, then 10%, and so on. All the while, the operations team monitors the system’s performance and error-rate dashboards. If any problems are detected, the automated pipeline can instantly roll back the change. This focus on automated provisioning, extensive testing, and reliable, staged rollouts is a hallmark of a mature DevOps practice.
MLOps in Action: The Recommendation System
Now, let’s see where MLOps comes in. Imagine that e-commerce platform wants to add a “Recommended for You” feature. This is a classic machine learning problem. A team of data scientists is assembled. They begin by building a data pipeline to collect user clickstream data. They then enter an experimental phase, training dozens of different models (like collaborative filtering or content-based models) to see which one provides the best recommendations. They use an experiment tracking tool to log the performance, parameters, and data version for every experiment. Once they find a winning model, the MLOps lifecycle begins. A Continuous Training (CT) pipeline is built. This automated pipeline is scheduled to run every night. It pulls the new clickstream data from the previous day, retrains the model on this fresh data, and validates its performance. If the new model is better than the one currently in production, a Continuous Deployment (CD) pipeline automatically packages the new model into a container and deploys it to the model serving platform. The system might even deploy it alongside the old model, running an A/B test by serving 20% of users the new recommendations. Finally, a Continuous Monitoring (CM) system watches the click-through-rate of the recommendations in real-time. If the performance suddenly drops (a sign of data drift), it alerts the team.
MLOps in Action: The Financial Fraud Detection System
A fraud detection system for a financial services company is a perfect and more critical use case for MLOps. The stakes are high, and the environment is adversarial. The “concept” of fraud is constantly changing as malicious actors invent new techniques to avoid detection. A static model trained on last year’s fraud patterns would be useless within weeks. This is a classic concept drift problem, and it demands a robust MLOps solution. The system cannot rely on nightly batch retraining; it must be more dynamic. An MLOps pipeline is set up to ingest transaction data in near-real-time. A monitoring system watches the model’s predictions. When a human analyst later flags a transaction as “fraud” (one that the model missed), this “ground truth” data is fed back into the system. This feedback loop is a trigger. The system might automatically add this new data to a “retraining set.” When enough new examples are collected, the CT pipeline is automatically triggered. It retrains, validates, and tests a new model on these new fraud patterns. After passing automated tests for bias and fairness, the CD pipeline “hot-swaps” the new model into the production environment with zero downtime, ensuring the application is always armed with the most up-to-date understanding of fraudulent behavior.
When Do You Need MLOps?
You should use MLOps when your product or process is data-centric and relies on machine learning models that are a key feature. If your model’s performance is critical to your business’s success, you need MLOps. This is especially true if the model needs to be frequently updated and retrained to adapt to changing data. Use cases like recommendation systems, fraud detection, autonomous driving, real-time analytics, natural language processing applications like chatbots, and image recognition all fall into this category. You also need MLOps when reproducibility and compliance are essential. In fields like finance or healthcare, you must be able to prove to a regulator why your model made a certain decision and be able to reproduce the exact model that was live on a specific date. This requires the rigorous versioning of data, code, and models that only an MLOps practice can provide. If your ML system is complex and you want to avoid accumulating massive “hidden technical debt,” you should invest in MLOps principles from the beginning.
When Do You Need DevOps?
A more straightforward question is: when do you need DevOps? In modern software development, the answer is “almost always.” If you are building any software application that focuses on web services, mobile apps, APIs, or SaaS tools, you need DevOps. This includes e-commerce platforms, content management systems, internal business tools, and virtually any application that will be updated over time. If you want to shorten your development cycles, improve your deployment frequency, and quickly deploy new features while ensuring the reliability of your system, you need DevOps. Even a team building an MLOps-heavy product still needs DevOps. The machine learning model is only one part of the product. There is still a web front-end, an API gateway, user authentication services, and databases that all need to be managed. The principles of CI/CD, IaC, and monitoring are the foundation that the entire application, including the ML parts, is built upon. DevOps is the default, foundational practice for all modern software development.
The Hybrid Case: An ML-Powered SaaS Application
The most common scenario in a tech-forward company is not “MLOps or DevOps,” but “MLOps and DevOps.” Consider a SaaS application that helps teams manage their marketing campaigns. The core application—user logins, project boards, and billing—is a traditional web app. The team building this core product follows a pure DevOps methodology. They have a CI/CD pipeline that deploys updates to the web server and API multiple times a day. Now, the company decides to add a new “smart” feature: a model that predicts the “likely engagement score” of a new email campaign before it is sent. This prediction is powered by a machine learning model. The team responsible for this feature follows an MLOps workflow. They build a data pipeline to pull historical campaign data, an experiment tracking system to log their models, and a CT pipeline to retrain the model weekly on new data. The output of their MLOps pipeline is a versioned model, which is deployed to a model-serving platform. The main DevOps pipeline then just needs to know the API address of this model. The two cycles work in parallel, one team deploying application code daily (DevOps) and the other deploying model updates weekly (MLOps), both contributing to the same end product.
The Cultural Challenge: Bridging Disparate Worlds
The greatest challenge in implementing MLOps, just as it was for DevOps, is not technical; it is cultural. DevOps was created to bridge the gap between two worlds: the “move fast” world of Developers and the “stay stable” world of Operations. MLOps adds even more complexity by introducing new groups, primarily data scientists and data engineers, who come from a third, entirely different culture. Data scientists often have a background in research, statistics, and academia. Their mindset is one of experimentation. Their goal is to explore, discover, and find the most accurate model, and their work is rarely “finished.” This experimental, research-and-development mindset can be in direct conflict with the stability mindset of Operations and the feature-delivery mindset of software developers. A data scientist’s priority is model performance, and they may be less focused on clean coding principles, test coverage, or an application’s production latency. MLOps is the cultural framework that attempts to forge a common language and a set of shared goals. It requires data scientists to learn about software engineering best practices (like writing testable code) and operations teams to learn about the unique needs of ML systems (like a model’s need to be retrained).
Resistance to Change: The Human Barrier
Both disciplines are disruptive by nature. They aim to automate workflows, enforce standards, and change the way people have always done their work. This disruption often leads to human resistance. In a DevOps transformation, developers who are used to “throwing their code over the wall” may resist the new responsibility of being on-call for production issues. Operations engineers who are used to manual, careful, gate-kept deployments may resist a fully automated pipeline that they perceive as risky. MLOps faces this same resistance, but amplified. Data scientists, who are used to the creative freedom of working in isolated notebooks, may see the new MLOps processes—like mandatory code reviews, versioning data, and logging all experiments—as a bureaucratic burden that slows them down. They might argue, “My job is to find a good model, not to be a software engineer.” Overcoming this resistance requires strong leadership and a clear articulation of the value of the new processes. It must be shown that MLOps does not stifle experimentation but rather enables it at scale, by making it reproducible, reliable, and deployable.
The Toolchain Complexity
Both disciplines require a diverse set of tools to function, and the complexity of integrating and maintaining this “toolchain” is a significant challenge. For DevOps, the tool landscape is relatively mature and standardized. An organization needs a source control system, a CI/CD automation server, an artifact repository, an IaC tool, a container orchestration platform, and a monitoring suite. While there are many options, the categories of tools are well-defined and understood. The MLOps tool landscape is less mature and far more fragmented. An MLOps team needs all the DevOps tools, plus a new stack of ML-specific tools. This includes data pipeline orchestrators, data version control systems, experiment tracking platforms, feature stores, model registries, and specialized model monitoring solutions. Integrating these disparate tools into a single, seamless workflow is a massive engineering effort. Teams must invest significant time in learning, configuring, and maintaining this complex and rapidly evolving ecosystem, all while the operations teams must figure out how to integrate it into the company’s existing infrastructure.
Scalability: Applications vs. Data
Both disciplines are deeply concerned with scalability, but they are often scaling different things. DevOps focuses primarily on scaling applications and services to meet user demand. The most common challenge is scaling a web application to handle a “flash crowd” of new users. This is often a “horizontal scaling” problem: since the application servers are often stateless, you can just add more of them. This is a well-understood challenge solved with load balancers and auto-scaling. MLOps must scale this plus two other, often harder, dimensions: data and computation. ML systems must be able to process, store, and version petabytes of data. This data growth presents enormous challenges in storage, processing, and governance. Furthermore, the model training process itself presents a unique computational challenge. Training a large deep learning model may require a distributed cluster of dozens or even hundreds of powerful GPUs working in parallel. This is a “vertical scaling” and “distributed computing” problem that is far more complex than simply adding more web servers. This makes the infrastructure for MLOps uniquely challenging to build and manage.
Addressing the Skills Gap: The Rise of the ML Engineer
The MLOps cultural and technical model requires a team of people with an incredibly diverse set of skills. You need data scientists who understand research, data engineers who understand distributed data processing, operations engineers who understand infrastructure, and software developers who understand application design. The problem is that very few individuals possess all these skills. A data scientist may be a statistics expert but know nothing about container orchestration. An operations engineer may be a cloud infrastructure expert but know nothing about model validation. This massive skills gap has led to the rise of a new, highly sought-after role: the Machine Learning Engineer (MLE). The MLE is a specialized “bridging” role. They are software engineers who have a deep understanding of the machine learning lifecycle. They are not typically the ones inventing new models (that is the data scientist’s job), but they are the ones who production-ize them. They take the data scientist’s prototype, rewrite it using clean, testable code, build the automated training and deployment pipelines, and configure the monitoring systems. This role is a direct product of the MLOps movement, created to fill the critical gap between data science and operations.
The Future: AI for DevOps (AIOps)
As MLOps extends DevOps to manage AI, a parallel movement is emerging: AIOps, or AI for DevOps. This involves applying machine learning techniques to improve the DevOps process itself. The monitoring and observability systems in a complex DevOps environment generate a massive, overwhelming flood of data—terabytes of logs, millions of metrics, and countless traces. It is impossible for a human operator to look at all of it. AIOps uses machine learning models to analyze this operational data. An anomaly detection model can sift through millions of log entries to find a single, critical error message that a human would have missed. A predictive model can analyze metrics to forecast a system failure before it happens. By applying ML to the operational data, AIOps promises to make DevOps systems more intelligent, more proactive, and more automated. This creates a fascinating symbiotic relationship: MLOps is the set of practices to manage AI, while AIOps is the practice of using AI to manage operations.
Conclusion:
In this comprehensive discussion, we have explored DevOps and MLOps, from their foundational principles to their practical challenges. DevOps is the cultural and technical methodology focused on the efficient and reliable development and deployment of traditional, code-centric software. MLOps is not a replacement but an essential extension of these principles, built to address the unique, data-driven, and probabilistic nature of machine learning. While DevOps focuses on code, MLOps focuses on a more complex lifecycle of data, models, and code. This introduces new challenges, new tools, and new team structures. However, both disciplines are united by the same fundamental goals: breaking down silos, automating everything, amplifying feedback loops, and fostering a culture of continuous learning. As more and new companies strive to integrate machine learning into their core products, mastering both DevOps and MLOps is no longer a luxury, but the fundamental requirement for building and maintaining the intelligent software of the future.